Focusing Viral Risk Ranking Tool on Prediction

Katherine Budeski, Marc Lipsitch
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Abstract

Preparing to rapidly respond to emerging infectious diseases is becoming ever more critical. "SpillOver: Viral Risk Ranking" is an open-source tool developed to evaluate novel wildlife-origin viruses for their risk of spillover from animals to humans and their risk of spreading in human populations. However, several of the factors used in the risk assessment are dependent on evidence of previous zoonotic spillover and/or sustained transmission in humans. Therefore, we performed a reanalysis of the "Ranking Comparison" after removing eight factors that require post-spillover knowledge and compared the adjusted risk rankings to the originals. The top 10 viruses as ranked by their adjusted scores also had very high original scores. However, the predictive power of the tool for whether a virus was a human virus or not as classified in the Spillover database deteriorated when these eight factors were removed. The area under the receiver operating characteristic curves (AUROC) for the original score, 0.94, decreased to 0.73 for the adjusted scores. Furthermore, we compared the mean and standard deviation of the human and non-human viruses at the factor level. Most of the excluded spillover-dependent factors had dissimilar means between the human and non-human virus groups compared to the non-spillover dependent factors, which frequently demonstrated similar means between the two groups with some exceptions. We concluded that the original formulation of the tool depended heavily on spillover-dependent factors to "predict" the risk of zoonotic spillover for a novel virus. Future iterations of the tool should take into consideration other non-spillover dependent factors and omit those that are spillover-dependent to ensure the tool is fit for purpose.
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将病毒风险排名工具的重点放在预测上
准备快速应对新出现的传染病变得越来越重要。"SpillOver:病毒风险排名 "是一种开源工具,用于评估源于野生动物的新型病毒从动物传染给人类的风险以及在人类中传播的风险。然而,风险评估中使用的几个因素取决于先前人畜共患病外溢和/或在人类中持续传播的证据。因此,我们对 "排名比较 "进行了重新分析,删除了需要了解溢出后情况的八个因素,并将调整后的风险排名与原来的排名进行了比较。按照调整后的分数排名,前 10 位病毒的原始分数也非常高。但是,如果剔除这八个因素,该工具对病毒是否属于溢出数据库中分类的人类病毒的预测能力就会下降。原始分数的接收者操作特征曲线下面积(AUROC)为 0.94,而调整后的分数则降至 0.73。此外,我们还比较了人类和非人类病毒在因子水平上的平均值和标准偏差。大多数被排除的外溢依赖因子在人类和非人类病毒组之间的平均值与当时的外溢依赖因子相比相差甚远,而后者在两组之间的平均值经常相似,但也有一些例外。我们的结论是,该工具最初的设计严重依赖于依赖溢出因子来 "预测 "新型病毒的人畜共患病溢出风险。该工具的未来迭代应考虑其他非溢出依赖因素,并省略溢出依赖因素,以确保该工具符合目的。
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